def test_load_iris():
"""
Tests :func:`fatf.utils.data.datasets.load_iris`.
"""
# Check the first, middle and last entry in the dataset.
n_samples = 150
n_features = 4
check_ind = np.array([0, 75, 149])
true_data = np.array([[5.1, 3.5, 1.4, 0.2],
[6.6, 3.0, 4.4, 1.4],
[5.9, 3.0, 5.1, 1.8]]) # yapf: disable
true_target = np.array([0, 1, 2])
target_names = np.array(['setosa', 'versicolor', 'virginica'])
feature_names = np.array([
'sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',
'petal width (cm)'
])
iris_data = fudd.load_iris()
assert not fuav.is_structured_array(iris_data['data'])
assert iris_data['data'].shape == (n_samples, n_features)
assert iris_data['target'].shape == (n_samples, )
assert np.array_equal(iris_data['target_names'], target_names)
assert np.array_equal(iris_data['feature_names'], feature_names)
assert np.isclose(iris_data['data'][check_ind, :], true_data).all()
assert np.isclose(iris_data['target'][check_ind], true_target).all()
"""
# Author: Kacper Sokol <*****@*****.**>
# License: new BSD
from pprint import pprint
import numpy as np
import fatf.utils.data.datasets as fatf_datasets
import fatf.utils.models as fatf_models
import fatf.transparency.predictions.counterfactuals as fatf_cf
print(__doc__)
# Load data
iris_data_dict = fatf_datasets.load_iris()
iris_X = iris_data_dict['data']
iris_y = iris_data_dict['target'].astype(int)
iris_feature_names = iris_data_dict['feature_names']
iris_class_names = iris_data_dict['target_names']
# Train a model
clf = fatf_models.KNN()
clf.fit(iris_X, iris_y)
# Create a Counterfactual Explainer
cf_explainer = fatf_cf.CounterfactualExplainer(
model=clf,
dataset=iris_X,
categorical_indices=[],
default_numerical_step_size=0.1)